Generates AWS architecture diagrams from CloudFormation templates, CLI output, or natural language descriptions.
Works with
Parses CloudFormation (YAML/JSON), AWS CLI JSON output, and text descriptions to extract VPCs, subnets, EC2 instances, RDS databases, Lambda functions, S3 buckets, security groups, load balancers, and other AWS services
Maps resource relationships including instances within subnets, security group attachments, IAM role bindings, and data flow between services
Converts AWS
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionaws-diagramsExecute the skills CLI command in your project's root directory to begin installation:
Fetches aws-diagrams from eraserlabs/eraser-io and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate aws-diagrams. Access via /aws-diagrams in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Generates architecture diagrams for AWS infrastructure from CloudFormation templates, AWS CLI output, or natural language descriptions.
Activate this skill when:
aws ec2 describe-instances)This skill generates AWS-specific diagrams by parsing AWS resources and calling the Eraser API directly:
/api/render/elements with diagramType: "cloud-architecture-diagram"When the user provides AWS infrastructure information:
Parse the Source
Resources section, identify types (AWS::EC2::Instance, etc.)aws commandsIdentify AWS Components
Map Relationships
Generate Eraser DSL Convert AWS resources to Eraser DSL:
[label: "VPC 10.0.0.0/16"]Example:
main-vpc [label: "VPC 10.0.0.0/16"] {
public-subnet [label: "Public Subnet"] {
web-server [icon: aws-ec2, label: "Web Server"]
load-balancer [icon: aws-elb]
}
private-subnet [label: "Private Subnet"] {
database [icon: aws-rds]
cache [icon: aws-elasticache]
}
}
data-bucket [icon: aws-s3]
function [icon: aws-lambda]
load-balancer -> web-server
web-server -> database
Make the HTTP Request
IMPORTANT: You MUST execute this curl command after generating the DSL. Never stop after generating DSL without making the API call.
CRITICAL: In the X-Skill-Source header below, you MUST replace the value with your AI agent name:
claudecursorchatgptgeminicurl -X POST https://app.eraser.io/api/render/elements \
-H "Content-Type: application/json" \
-H "X-Skill-Source: eraser-skill" \
-H "Authorization: Bearer ${ERASER_API_KEY}" \
-d '{
"elements": [{
"type": "diagram",
"id": "diagram-1",
"code": "<your generated DSL>",
"diagramType": "cloud-architecture-diagram"
}],
"scale": 2,
"theme": "${ERASER_THEME:-dark}",
"background": true
}'
Track Sources During Analysis
As you analyze files and resources to generate the diagram, track:
infra/main.tf - VPC and subnet definitions)Handle the Response
CRITICAL: Minimal Output Format
Your response MUST always include these elements with clear headers:
Diagram Preview: Display with a header
## Diagram

Use the ACTUAL imageUrl from the API response.
Editor Link: Display with a header
## Open in Eraser
[Edit this diagram in the Eraser editor]({createEraserFileUrl})
Use the ACTUAL URL from the API response.
Sources section: Brief list of files/resources analyzed (if applicable)
## Sources
- `path/to/file` - What was extracted
Diagram Code section: The Eraser DSL in a code block with eraser language tag
## Diagram Code
```eraser
{DSL code here}
Learn More link: You can learn more about Eraser at https://docs.eraser.io/docs/using-ai-agent-integrations
Additional content rules:
The default output should be SHORT. The diagram image speaks for itself.
Resources:
MyVPC:
Type: AWS::EC2::VPC
Properties:
CidrBlock: 10.0.0.0/16
PublicSubnet:
Type: AWS::EC2::Subnet
Properties:
VpcId: !Ref MyVPC
CidrBlock: 10.0.1.0/24
WebServer:
Type: AWS::EC2::Instance
Properties:
InstanceType: t3.micro
SubnetId: !Ref PublicSubnet
MyBucket:
Type: AWS::S3::Bucket
Properties:
BucketName: my-app-bucket
MyFunction:
Type: AWS::Lambda::Function
Properties:
Runtime: python3.9
Handler: index.handler
MyDatabase:
Type: AWS::RDS::DBInstance
Properties:
Engine: postgres
DBInstanceClass: db.t3.micro
Parses CloudFormation:
Generates DSL showing AWS service diversity:
MyVPC [label: "VPC 10.0.0.0/16"] {
PublicSubnet [label: "Public Subnet 10.0.1.0/24"] {
WebServer [icon: aws-ec2, label: "EC2 t3.micro"]
}
}
MyBucket [icon: aws-s3, label: "S3 my-app-bucket"]
MyFunction [icon: aws-lambda, label: "Lambda python3.9"]
MyDatabase [icon: aws-rds, label: "RDS PostgreSQL db.t3.micro"]
WebServer -> MyBucket
MyFunction -> MyDatabase
WebServer -> MyDatabase
Important: All label text must be on a single line within quotes. AWS-specific: Include service icons, show data flows between services, group by VPC when applicable.
Calls /api/render/elements with diagramType: "cloud-architecture-diagram"
User runs: aws ec2 describe-instances
Provides JSON output
Parses JSON to extract:
Formats and calls API
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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aws-diagrams has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend aws-diagrams for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: aws-diagrams is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: aws-diagrams is the kind of skill you can hand to a new teammate without a long onboarding doc.
aws-diagrams fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
aws-diagrams is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added aws-diagrams from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
aws-diagrams reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added aws-diagrams from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added aws-diagrams from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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